Adapting Resiliency of Enterprise Object Storage Systems

ABSTRACT

Various implementations disclosed herein enable managing a resiliency factor of an object stored in an enterprise object storage system. For example, in various implementations, a method of adjusting a realized resiliency factor of an object based on a target resiliency factor for the object is performed by an ingest entity of a storage system that includes a cluster of storage entities. The ingest entity includes a non-transitory computer readable storage medium, and one or more processors. In various implementations, the method includes obtaining a target resiliency factor for an object. In various implementations, the method includes determining whether or not to adjust a realized resiliency factor of the object based on the target resiliency factor. In various implementations, the method includes adjusting the realized resiliency factor of the object to an adjusted resiliency factor in response to determining to adjust the realized resiliency factor.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.15/264,405, filed on Sep. 13, 2016, the contents of which are herebyincorporated by reference for all purposes.

TECHNICAL FIELD

The present disclosure relates generally to enterprise object storagesystems, and in particular, to managing data loss resiliency ofenterprise object storage systems.

BACKGROUND

Some previously available storage systems provide fault tolerancethrough data mirroring. With data mirroring, multiple copies of anobject are stored on a vault disk and again on different drives, so thata drive failure can only damage at most one copy of the data. Thedownside of data mirroring is that it is expensive due to beingresource-intensive. For example, to be resilient to one failure, astorage system that utilizes data mirroring has to double the disk spaceavailable. Similarly, to be resilient to two failures, a storage systemthat utilizes data mirroring has to triple the disk space available.Another particular problem with data mirroring is that it results in apolicy conflict for recording various media programs. Specifically, whenrecording a program on behalf of a customer, the Digital MillenniumCopyright Act (DMCA) provides that one and only one unique instance ofthe data may be created for the customer. Therefore in this situation,data mirroring for the sake of providing fault tolerance violatescopyright and associated fair use restrictions.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the present disclosure can be understood by those of ordinaryskill in the art, a more detailed description may be had by reference toaspects of some illustrative implementations, some of which are shown inthe accompanying drawings.

FIGS. 1A-1B are schematic diagrams of a storage system environment thatincludes a fault-tolerant enterprise object storage system incommunication with a client device in accordance with someimplementations.

FIG. 2 is a block diagram of an ingest entity of the fault-tolerantenterprise object storage system in accordance with someimplementations.

FIG. 3 is a flowchart representation of a method of adjusting a realizedresiliency factor of an object based on a target resiliency factor forthe object in accordance with some implementations.

FIG. 4A is a flowchart representation of a method of determining whetheror not to adjust a realized resiliency factor of an object upondetecting a decrease in a number of available storage entities inaccordance with some implementations.

FIG. 4B is a flowchart representation of a method of determining whetheror not to adjust a realized resiliency factor of an object upondetecting an increase in a number of available storage entities inaccordance with some implementations.

FIG. 5 is a block diagram of a server system enabled with variousmodules that are provided to adjust a realized resiliency factor of anobject based on a target resiliency factor for the object in accordancewith some implementations.

In accordance with common practice the various features illustrated inthe drawings may not be drawn to scale. Accordingly, the dimensions ofthe various features may be arbitrarily expanded or reduced for clarity.In addition, some of the drawings may not depict all of the componentsof a given system, method or device. Finally, like reference numeralsmay be used to denote like features throughout the specification andfigures.

DESCRIPTION OF EXAMPLE EMBODIMENTS

Numerous details are described herein in order to provide a thoroughunderstanding of the illustrative implementations shown in theaccompanying drawings. However, the accompanying drawings merely showsome example aspects of the present disclosure and are therefore not tobe considered limiting. Those of ordinary skill in the art willappreciate from the present disclosure that other effective aspectsand/or variants do not include all of the specific details of theexample implementations described herein. While pertinent features areshown and described, those of ordinary skill in the art will appreciatefrom the present disclosure that various other features, includingwell-known systems, methods, components, devices, and circuits, have notbeen illustrated or described in exhaustive detail for the sake ofbrevity and so as not to obscure more pertinent aspects of the exampleimplementations disclosed herein.

OVERVIEW

Some previously available storage systems provide fault tolerancethrough erasure coding. Typically, a storage system that utilizeserasure coding stores an object across various storage entities. Inaddition to storing object data, some previously available storagesystems synthesize and store parity information for the object. Suchstorage systems are typically capable of detecting a loss of objectdata. Upon detecting the loss of object data, the storage systemutilizes the parity information and the remaining object data toreconstruct the lost object data. Some previously available storagesystems operate according to a predefined resiliency overhead thatindicates a percentage of storage space that the storage systemallocates to store parity information. Typically, a higher resiliencyoverhead makes the storage system more resilient to failures that resultin data loss. However, a higher resiliency overhead tends to increasethe cost of storing objects because maintaining a higher resiliencyoverhead is more resource-intensive.

Various implementations disclosed herein enable adjusting a realizedresiliency factor associated with an object based on a target resiliencyfactor for the object. For example, in various implementations, a methodof adjusting a realized resiliency factor of an object is performed byan ingest entity of a fault-tolerant enterprise object storage system(“storage system”, hereinafter for the sake of brevity). In someimplementations, the storage system is configured to synthesize paritydata for an object in order to protect the object from a data lossevent. In various implementations, the storage system includes aplurality of storage entities, and one or more computing processors. Insome implementations, each storage entity is configured to store objectdata and/or parity data on a block basis. In various implementations,the method includes obtaining a target resiliency factor for an objector the storage system as a whole. In some implementations, the targetresiliency factor indicates a target number of data storage entities forstoring object data (e.g., a target data storage entity count) and atarget number of parity storage entities for storing parity information(e.g., a target parity storage entity count).

In various implementations, the method includes determining whether ornot to adjust a realized resiliency factor of the object based on thetarget resiliency factor. In some implementations, the realizedresiliency factor indicates a first number of data storage entitiesconfigured to store object data (e.g., a realized data storage entitycount) and a first number of parity storage entities configured to storeparity information (e.g., a realized parity storage entity count). Invarious implementations, the method includes adjusting the realizedresiliency factor to an adjusted resiliency factor. In someimplementations, the adjusted resiliency factor includes at least one ofa second number of data storage entities (e.g., an adjusted data storageentity count) and a second number of parity storage entities (e.g., anadjusted parity storage entity count) in order to providefault-tolerance for the object data within a threshold range of thetarget resiliency factor.

EXAMPLE EMBODIMENTS

FIGS. 1A and 1B are schematic diagrams of a storage system environment10 in accordance with some implementations. While pertinent features areshown, those of ordinary skill in the art will appreciate from thepresent disclosure that various other features have not been illustratedfor the sake of brevity and so as not to obscure more pertinent aspectsof the example implementations disclosed herein. To that end, as anon-limiting example, the storage system environment 10 includes aclient device 20, and a fault-tolerant enterprise object storage system100 (storage system 100, hereinafter). In various implementations, theclient device 20 and the storage system 100 communicate via a network(not shown), such as portions of the Internet and/or a private network.

In operation, the storage system 100 is utilized to store variousobjects. In some implementations, an object refers to a data asset. Insome implementations, an object includes a data asset that ispresentable to a user via the client device 20. For example, the objectincludes a video file that represents a movie or a TV show, an audiofile that represents a song, a text file, etc. In variousimplementations, the object includes a file of any file type (e.g.,.mov, .wma, .mp4, .avi, .mp3, .jpg, .txt, .doc, .docx, .xls, .ppt, etc.)In some implementations, an object includes a data asset that representsa set of computer-readable instructions that are executable at theclient device 20. For example, in some implementations, the objectincludes a native application that is downloaded and installed at theclient device 20, a browser plugin, etc.

In various implementations, the storage system 100 includes a cluster ofstorage entities 110-1, 110-2 . . . 110-10 (collectively, storageentities 110), and an ingest entity 120. In the example of FIGS. 1A-1B,the storage system 100 includes ten storage entities 110. However, insome examples, the storage system 100 includes a fewer number of storageentities 110, or a greater number of storage entities 110. The storageentities 110 store objects. For example, as illustrated in FIG. 1A, someof the storage entities 110 collectively store an object 112. In variousimplementations, some of the storage entities 110 are configured tostore object data for the object 112, and some of the remaining storageentities 110 are configured to store parity data for the object 112. Insome implementations, the storage entities 110 that store object datafor the object 112 are referred to as data storage entities, and thestorage entities 110 that store parity data for the object 112 arereferred to as one or more parity storage entities. The storage system100 utilizes any suitable combination of methods and systems forsynthesizing the parity data. In various implementations, the storagesystem 100 utilizes the parity data to recover (e.g., rebuild,reconstruct, restore, and/or repair) the object 112 in the event of adata loss. In some implementations, the storage entities 110 and theingest entity 120 are network entities such as servers.

In some implementations, a storage entity 110 includes one or morecomputer readable storage mediums. For example, the storage entity 110includes solid state memory devices, hard disk memory devices, opticaldisk drives, read-only memory and/or nanotube-based storage devices. Insome implementations, the storage entities 110 includes servers thatexecute computer-readable instructions. In various implementations, astorage entity 110 includes various blocks (not shown). For example, insome implementations, a storage entity 110 that stores object data(e.g., a data storage entity such as the storage entities 110-1, 110-2 .. . 110-4) includes data blocks for storing the object data. Similarly,a storage entity 110 that stores parity data (e.g., a parity storageentity such as the storage entity 110-5) includes parity blocks forstoring the parity data. As described herein, in variousimplementations, a block refers to the smallest addressable block ofmemory (e.g., the smallest allocation unit of data) in a storage entity110. In some implementations, the blocks are identically-sized (e.g., 2MB each) for processing convenience.

In various implementations, the ingest entity 120 serves as an interfacefor the storage system 100. The ingest entity 120 receives/transmitsdata from/to any device that is external to the storage system 100.Specifically, the ingest entity 120 receives/transmits data from/to theclient device 20. In various implementations, receiving/transmittingdata includes receiving/transmitting the objects. Alternatively and/oradditionally, receiving/transmitting data includesreceiving/transmitting instructions. In some implementations, theinstructions include operations that are performed in relation to theobjects. Example instructions include writing an object, reading anobject, deleting an object, copying an object, etc. In someimplementations, the ingest entity 120 includes hardware and/or softwarethat enables the ingest entity 120 to perform various operationsdescribed herein. In some examples, the ingest entity 120 is implementedby a server system (e.g., as illustrated in FIG. 5). In someimplementations, the ingest entity 120 is configured to operate as oneof the storage entities 110. Put another way, in some implementations,one of the storage entities 110 is configured to operate as the ingestentity 120.

In various implementations, the storage system 100 utilizes variousmethods and systems associated with distributed erasure coding. In someimplementations, the storage system 100 distributes an object acrossmultiple storage entities 110. For example, the storage system 100stores the first 2 MB of the object data at storage entity 110-1, thenext 2 MB of the object data at storage entity 110-2, etc. In someimplementations, the storage system 100 distributes the object acrossmultiple storage entities 110 even if the object is small enough to bestored at a single storage entity 110. Distributing the object data andthe parity data across multiple storage entities 110 reduces the risk oflosing the entire object in the event of a data loss. To that end, invarious implementations, the object 112 is a data asset (e.g., a dataitem) that is stored in accordance with distributed erasure coding.

In various implementations, the object 112 is associated with a realizedresiliency factor 130. In various implementations, the realizedresiliency factor 130 indicates a number of storage entities 110 thatthe storage system 100 has allocated to store the object data, and anumber of storage entities 110 that the storage system 100 has allocatedto store the parity data for the object 112. In some implementations,the number of storage entities 110 that store the object data isreferred to as a realized data storage entity count 130 d, and thenumber of storage entities 110 that store parity data is referred to asa realized parity storage entity count 130 p. In the example of FIG. 1A,the object 112 is stored across five storage entities (e.g., storageentities 110-1, 110-2 . . . 110-5). In this example, four of the fivestorage entities 110 store object data, and one of the five storageentities 110 stores parity data. For example, as illustrated, thestorage entities 110-1, 110-2 . . . 110-4 store object data and thestorage entity 110-5 stores parity data. Thus, in the example of FIG.1A, the realized data storage entity count 130 d for the object 112 isfour, and the realized parity storage entity count 130 p is one. In somescenarios, the realized resiliency factor 130 is expressed as a ratio ofthe realized data storage entity count 130 d and the realized paritystorage entity count 130 p (e.g., 4:1 for the object 112 shown in FIG.1A).

In various implementations, the realized resiliency factor 130 differsfor various objects that are stored in the storage system 100. In otherwords, in various implementations, the storage system 100 stores objectsaccording to different realized resiliency factors 130. In variousimplementations, the realized resiliency factor 130 indicates a realizedresiliency overhead associated with the object 112. In variousimplementations, the realized resiliency overhead is determined (e.g.,computed) by dividing the realized parity storage entity count 130 p forthe object by the realized data storage entity count 130 d for theobject. In some implementations, the realized resiliency overhead isexpressed as a percentage that represents the percentage of storageentities 110 allocated for storing parity data for the object 112. Inthe example of FIG. 1A, the realized resiliency overhead for the object112 is 0.25 (¼) or 25%.

In various implementations, the storage system 100 (e.g., the ingestentity 120) obtains a target resiliency factor 30 for the object 112.For example, as illustrated in FIG. 1A, in some implementations, thestorage system 100 receives the target resiliency factor 30 from theclient device 20. In some implementations, the target resiliency factor30 indicates a target data storage entity count 30 d (e.g., a targetnumber of data storage entities for storing the object data), and atarget parity storage entity count 30 p (e.g., a target number of paritystorage entities for storing the parity data). In some examples, thestorage system 100 receives the target resiliency factor 30 for theobject 112 as part of a write request to write the object 112 into thestorage system 100. Additionally and/or alternatively, in some examples,the storage system 100 receives the target resiliency factor 30 for theobject 112 after the object 112 is written into the storage system 100.Moreover, in some examples, the storage system 100 obtains the targetresiliency factor 30 by retrieving the target resiliency factor 30 froma non-transitory memory.

In some implementations, the target resiliency factor 30 indicates atarget number of data storage allocations, and/or a target number ofparity storage allocations. In some examples, the target number of datastorage allocations refers to a target number of storage disks to storethe object data, and the target number of parity storage allocationsrefers to a target number of storage disks to store the parity data. Insome examples, the target number of data storage allocations refers to atarget number of data blocks to store the object data, and the targetnumber of parity storage allocations refers to a target number of parityblocks to store the parity data.

As illustrated in FIG. 1B, in various implementations, the storagesystem 100 adjusts the realized resiliency factor 130 to an adjustedresiliency factor 230 based on the target resiliency factor 30. In someimplementations, the adjusted resiliency factor 230 includes an adjusteddata storage entity count 230 d (e.g., an adjusted number of datastorage entities for storing the object data), and/or an adjusted paritystorage entity count 230 p (e.g., an adjusted number of parity storageentities for storing the parity data). In the example of FIG. 1B, theadjusted resiliency factor 230 is 6:2. Put another way, in the exampleof FIG. 1B, the adjusted data storage entity count 230 d is six, and theadjusted parity storage entity count 230 p is two. Moreover, in theexample of FIG. 1B, the adjusted resiliency overhead is 0.33 ( 2/6) or33%. A person of ordinary skill in the art will appreciate that sincethe adjusted parity storage entity count 230 p is greater than therealized parity storage entity count 130 p (between the two examples),the object 112 has become more resilient to data loss events. In variousimplementations, the storage system 100 adjusts the realized resiliencyfactor 130 to the adjusted resiliency factor 230 in order to providefault-tolerance for the object 112 within a threshold range of thetarget resiliency factor 30. For example, in some implementations, thestorage system 100 sets the adjusted resiliency factor 230 equal to thetarget resiliency factor 30. In some implementations, the storage system100 adjusts the realized resiliency factor 130 to the adjustedresiliency factor 230 in order to attain a resiliency overhead that iscloser to a target resiliency overhead indicated by the targetresiliency factor 30.

In some implementations, the storage system 100 transmits an adjustmentconfirmation 232 to the client device 20 upon adjusting the realizedresiliency factor 130 to the adjusted resiliency factor 230. In someexamples, the adjustment confirmation 232 indicates the adjustedresiliency factor 230. For example, as illustrated in FIG. 1B, theadjustment confirmation 232 includes the adjusted data storage entitycount 230 d and the adjusted parity storage entity count 230 p. In someimplementations, the adjustment confirmation 232 includes otherinformation. For example, in some scenarios, the adjustment confirmation232 includes the adjusted resiliency overhead for the object 112.

In some implementations, the storage entities 110 are implemented on thesame computing device. For example, in some implementations, the storageentities 110 are storage disks. In such implementations, multiplestorage entities 110 are enclosed in a single housing. Alternatively, insome implementations, the storage entities 110 are implemented ondifferent computing devices. In some implementations, the storage system100 is a distributed storage system including multiple computing devicesnetworked over multiple locations. In some implementations, the storagesystem 100 is configured to store video data associated with multicast(e.g., broadcast) content. In other words, the storage system 100 servesas a digital video recorder (DVR). In some implementations, the storagesystem 100 serves as a cloud-based DVR, since the storage system 100 iscapable of servicing read requests and write requests that the storagesystem 100 receives.

In various implementations, the client device 20 includes any suitablecomputing device, such as a computer, a laptop computer, a tabletdevice, a netbook, an internet kiosk, a personal digital assistant, amobile phone, a smartphone, a wearable, a gaming device, a computerserver, etc. In some implementations, the client device 20 includes oneor more processors, one or more types of memory, a display and/or otheruser interface components such as a keyboard, a touch screen display, amouse, a track-pad, a digital camera and/or any number of supplementaldevices to add functionality. In some implementations, a client device20 includes a suitable combination of hardware, software and firmwareconfigured to provide at least some of protocol processing, modulation,demodulation, data buffering, power control, routing, switching, clockrecovery, amplification, decoding, and error control.

FIG. 2 is a block diagram of the ingest entity 120 in accordance withsome implementations. In various implementations, the ingest entity 120includes a resiliency management module 122, a resiliency determinationmodule 124, a resiliency adjustment module 126, and a data store 128. Invarious implementations, the resiliency management module 122, theresiliency determination module 124, and the resiliency adjustmentmodule 126 are implemented in hardware (e.g., as one or more applicationspecific integrated circuits (ASICs)) and/or in software (e.g., as oneor more sets of computer readable instructions that are executed by oneor more central processing units).

In various implementations, the resiliency determination module 124determines the realized resiliency factor 130 for the object 112. Forexample, in some implementations, the resiliency determination module124 determines the realized data storage entity count 130 d, and therealized parity storage entity count 130 p for the object 112. In someimplementations, the resiliency determination module 124 queries thestorage entities 110 to determine where various data segments (e.g., oneor more data blocks) and parity segments (e.g., one or more parityblocks) of the object 112 are stored. In some examples, the resiliencydetermination module 124 transmits locate requests to the storageentities 110, and receives locate responses that indicate the locationsof various data segments and parity segments. In some scenarios, thelocate requests include an object identifier (ID) for the object 112. Invarious implementations, the resiliency determination module 124determines the realized data storage entity count 130 d by counting thenumber of storage entities 110 that store data segments of the object112. Similarly, the resiliency determination module 124 determines therealized parity storage entity count 130 p by counting the number ofstorage entities 110 that store parity segments of the object 112. Insome implementations, the resiliency determination module 124 providesthe realized resiliency factor 130 to the resiliency management module122. Additionally and/or alternatively, the resiliency determinationmodule 124 stores the realized resiliency factor 130 in the data store128. In various implementations, the resiliency determination module 124determines the realized resiliency factor 130 periodically, and/or inresponse to a request (e.g., from the resiliency management module 122).

In various implementations, the resiliency management module 122 managesa resiliency factor of the object 112. In various implementations, theresiliency management module 122 obtains the target resiliency factor 30for the object 112, and determines whether or not to adjust the realizedresiliency factor 130 of the object 112 based on the target resiliencyfactor 30. In some implementations, the resiliency management module 122determines a number of available storage entities 110 in the storagesystem 100. Upon determining the number of available storage entities110, the resiliency management module 122 determines whether the numberof available storage entities 110 is sufficient to realize the targetresiliency factor 30. For example, the resiliency management module 122determines whether the number of available storage entities 110 isgreater than or equal to a sum of the target data storage entity count30 d and the target parity storage entity count 30 p. If the number ofavailable storage entities 110 is sufficient to realize the targetresiliency factor 30, then the resiliency management module 122determines to adjust the realized resiliency factor 130 to an adjustedresiliency factor 230. Moreover, if the number of available storageentities 110 is sufficient to realize the target resiliency factor 30,then the resiliency management module 122 sets the adjusted resiliencyfactor 230 equal to the target resiliency factor 30.

In some implementations, the resiliency management module 122 determinesthat the number of available storage entities 110 is not sufficient torealize the target resiliency factor 30. For example, the resiliencymanagement module 122 determines that the number of available storageentities 110 is less than the sum of the target data storage entitycount 30 d and the target parity storage entity count 30 p. In suchimplementations, the resiliency management module 122 determines whetherthe number of available storage entities 110 is sufficient to adjust therealized resiliency factor 130 to an adjusted resiliency factor 230 thatis closer to the target resiliency factor 30. If the number of availablestorage entities 110 is sufficient to adjust the realized resiliencyfactor 130 closer to the target resiliency factor 30, then theresiliency management module 122 determines an adjusted resiliencyfactor 230 that is closer to the target resiliency factor 30. In someimplementations, the resiliency management module 122 determines thatthe number of available storage entities 110 is not sufficient to adjustthe realized resiliency factor 130 equal to or closer to the targetresiliency factor 30. In such implementations, the resiliency managementmodule 122 determines not to adjust the realized resiliency factor 130.In various implementations, the resiliency management module 122transmits the adjusted resiliency factor 230 to the resiliencyadjustment module 126. In various implementations, the resiliencymanagement module 122 selects an adjusted resiliency factor 230 that iswithin a threshold range of the target resiliency factor 30. In otherwords, in various implementations, the resiliency management module 122determines to adjust the realized resiliency factor 130 in order toprovide fault-tolerance for the object 112 within a threshold range ofthe target resiliency factor 30.

In various implementations, the resiliency adjustment module 126 adjuststhe realized resiliency factor 130 to the adjusted resiliency factor230. In some implementations, the resiliency adjustment module 126re-distributes the object data for the object 112 across a number ofstorage entities 110 indicated by the adjusted data storage entity count230 d. In some implementations, the resiliency adjustment module 126synthesizes parity information for the object 112 based on there-distributed object data and the adjusted resiliency factor 230. Uponsynthesizing the parity information, the resiliency adjustment module126 stores the parity information across a number of storage entities110 indicated by the adjusted parity storage entity count 230 p. In someimplementations, the resiliency adjustment module 126 transmits theadjustment confirmation 232 to the client device 20. In some examples,the adjustment confirmation 232 indicates the adjusted resiliency factor230 for the object 112. In some implementations, the resiliencyadjustment module 126 determines whether adjusting the realizedresiliency factor 130 to the adjusted resiliency factor 230 would resultin the object being mirrored. In such implementations, the resiliencyadjustment module 126 adjusts the realized resiliency factor 130 to theadjusted resiliency factor 230 upon determining that the adjustmentwould not result in the object 112 being mirrored. However, if adjustingthe realized resiliency factor 130 to the adjusted resiliency factor 230would result in the object 112 being mirrored, then the resiliencyadjustment module 126 determines not to adjust the realized resiliencyfactor 130 in order to avoid violation of copyright law. In variousimplementations, the resiliency adjustment module 126 adjusts therealized resiliency factor 130 to the adjusted resiliency factor 230.

In various implementations, the data store 128 stores information (e.g.,metadata) associated with objects that are stored in the storage system100. For example, in some implementations, the data store 128 storesobject identifiers (IDs) for objects that are stored in the storageentities 110. In some implementations, the data store 128 stores thetarget resiliency factor 30 for the object 112. In such implementations,the resiliency management module 122 obtains the target resiliencyfactor 30 by retrieving the target resiliency factor 30 from the datastore 128. In some implementations, the data store 128 stores therealized resiliency factor 130 for the object 112. In suchimplementations, the resiliency determination module 124 and/or theresiliency management module 122 retrieve the realized resiliency factor130 from the data store 128. In some implementations, the resiliencymanagement module 122 periodically obtains the target resiliency factor30 from the data store 128 and determines whether or not to adjust therealized resiliency factor 130 based on the target resiliency factor 30.To that end, the data store 128 includes one or more databases, tables(e.g., look-up tables), indices (e.g., inverted indices), and/or anyother suitable data structure.

FIG. 3 is a flowchart representation of a method 300 of adjusting arealized resiliency factor of an object based on a target resiliencyfactor for the object in accordance with some implementations. Invarious implementations, the method 300 is implemented as a set ofcomputer readable instructions that are executed at a storage system(e.g., the storage system 100 shown in FIGS. 1A-1B). For example, invarious implementations, the method 300 is performed by an ingest entityof the storage system (e.g., the ingest entity 120 shown in FIGS. 1A, 1Band 2). Briefly, the method 300 includes obtaining a target resiliencyfactor for an object, determining whether or not to adjust a realizedresiliency factor of the object based on the target resiliency factor,and adjusting the realized resiliency factor to an adjusted resiliencyfactor upon determining to adjust the realized resiliency factor.

As represented by block 310, in various implementations, the method 300includes obtaining a target resiliency factor for an object (e.g., thetarget resiliency factor 30 shown in FIGS. 1A and 2). As represented byblock 310 a, in some implementations, the method 300 includes receivingthe target resiliency factor as part of a write request to write theobject into the storage system. As represented by block 310 b, in someimplementations, the method 300 includes receiving the target resiliencyfactor after the object is stored in the storage system. In someexamples, the object is stored according to a first target resiliencyfactor, and the method 300 includes receiving a second target resiliencyfactor that is different from the first target resiliency factor. Asrepresented by block 310 c, in some implementations, the method 300includes retrieving the target resiliency factor from a non-transitorymemory (e.g., the data store 128 shown in FIG. 2). In some examples, anobject is stored according to a resiliency factor that is different fromthe target resiliency factor for the object (e.g., due to a lack ofavailable storage entities). In such examples, obtaining the targetresiliency factor includes periodically retrieving the target resiliencyfactor from the memory in order to determine whether the targetresiliency factor is realizable (e.g., due to a change in the number ofavailable storage entities). In various implementations, obtaining thetarget resiliency factor includes obtaining a target data storage entitycount, and a target parity storage entity count (e.g., the target datastorage entity count 30 d, and the target parity storage entity count 30p, respectively, shown in FIGS. 1A and 2).

As represented by block 320, in various implementations, the method 300includes determining whether or not to adjust a realized resiliencyfactor of the object based on the target resiliency factor for theobject. As represented by block 322, in some implementations, the method300 includes determining whether a number of available storage entitiesis greater than or equal to a sum of the target data storage entitycount and the target parity storage entity count. In other words, insome implementations, the method 300 includes determining whether thenumber of available storage entities is sufficient to realize the targetresiliency factor. In some examples, determining the number of availablestorage entities includes identifying the storage entities that areonline (e.g., in service, and ready to store object data and/or paritydata). In some implementations, if the number of available storageentities is greater than or equal to the sum then the method 300proceeds to block 324, otherwise the method 300 proceeds to block 326.Put another way, if the number of available storage entities issufficient to realize the target resiliency factor then the method 300proceeds to block 324, else the method 300 proceeds to block 326. Asrepresented by block 324, in various implementations, the method 300includes determining to adjust the realized resiliency factor to anadjusted resiliency factor that is equal to the target resiliencyfactor.

As represented by block 326, in some implementations, the method 300includes determining whether the number of available storage entities issufficient to adjust the realized resiliency factor to an adjustedresiliency factor that is closer to the target resiliency factor. Insome examples, the method 300 includes determining whether the number ofavailable storage entities is sufficient to realize a resiliency factorthat is within a threshold range of the target resiliency factor (e.g.,within 10% of the target resiliency factor). In other words, in someimplementations, the method 300 includes determining whether there areenough storage entities available to adjust the realized resiliencyfactor to an adjusted resiliency factor that is within a threshold rangeof the target resiliency factor. In various implementations, if anadjusted resiliency factor that is closer to the target resiliencyfactor is attainable, then the method 300 proceeds to block 328,otherwise the method 300 proceeds to block 330.

As represented by block 328, in various implementations, the method 300includes determining to adjust the realized resiliency factor to anadjusted resiliency factor that is closer to the target resiliencyfactor (e.g., within a threshold range of the target resiliency factor).In various implementations, the adjusted resiliency factor being withina threshold range of the target resiliency factor refers to an adjustedresiliency overhead being within a threshold range of a targetresiliency overhead. In various implementations, upon determining toadjust the realized resiliency factor, the method 300 includesdetermining the adjusted resiliency factor. In some implementations,determining the adjusted resiliency factor includes selecting aresiliency factor that is as close as possible to the target resiliencyfactor given the number of available storage entities. In variousimplementations, the adjusted resiliency factor is a function of thetarget resiliency factor and the number of available storage entities.In some implementations, the adjusted resiliency factor includes anadjusted data storage entity count that is different from (e.g., greaterthan, or less than) the realized data storage entity count. Additionallyand/or alternatively, in some implementations, the adjusted resiliencyfactor includes an adjusted parity storage entity count that isdifferent from (e.g., greater than, or less than) the realized paritystorage entity count.

As represented by block 330, in various implementations, the method 300includes determining not to adjust the realized resiliency factor. Insome implementations, the method 300 includes determining not to adjustthe realized resiliency factor if the number of the available storageentities is not sufficient to realize an adjusted resiliency factor thatis closer to the target resiliency factor. In some implementations, themethod 300 includes determining not to adjust the realized resiliencyfactor if the number of available storage entities is not sufficient torealize an adjusted resiliency factor that is within a threshold rangeof the target resiliency factor. In some implementations, the method 300includes determining not to adjust the realized resiliency factor if adifference between the adjusted resiliency factor and the realizedresiliency factor is less than a threshold difference (e.g., less than2% of the realized resiliency factor).

As represented by block 360, in various implementations, the method 300includes adjusting the realized resiliency factor to an adjustedresiliency factor upon determining to adjust the realized resiliencyfactor. As described herein, in some implementations, the method 300includes adjusting the realized resiliency factor to an adjustedresiliency factor that is equal to the target resiliency factor. In someimplementations, the method 300 includes adjusting the realizedresiliency factor to an adjusted resiliency factor that is closer to thetarget resiliency factor. In some implementations, the method 300includes adjusting the realized resiliency factor to an adjustedresiliency factor that is within a threshold range of the targetresiliency factor.

As represented by block 362, in various implementations, the method 300includes re-distributing object data based on the adjusted resiliencyfactor. In some implementations, the method 300 includes storing theobject data across a number of storage entities that is equal to theadjusted data storage entity count. In various implementations, theadjusted data storage entity count is greater than the realized datastorage entity count. In such implementations, the method 300 includesmoving some of the object data to storage entities that were notpreviously storing the object data, so that the object data is spreadacross a number of storage entities that is equal to the adjusted datastorage entity count. In some implementations, the adjusted data storageentity count is less than the realized data storage entity count. Insuch implementations, the method 300 includes removing the object datafrom the storage entities that are in excess of the adjusted datastorage entity count, and limiting the object data to a number ofstorage entities that is equal to the adjusted data storage entitycount.

As represented by block 364, in various implementations, the method 300includes synthesizing parity information based on the re-distributeddata and the adjusted resiliency factor. In some implementations, themethod 300 includes storing the parity information in a number ofstorage entities that is equal to the adjusted parity storage entitycount. A person of ordinary skill in the art will appreciate that, insome implementations, a combination of various methods and systems areutilized to synthesize the parity information. In some implementations,the method 300 includes transmitting an adjustment confirmation to theclient device (e.g., the adjustment confirmation 232 shown in FIG. 1B).In some examples, the adjustment confirmation indicates the adjustedresiliency factor. For example, in some scenarios, the adjustmentconfirmation includes the adjusted data storage entity count and/or theadjusted parity storage entity count.

FIG. 4A is a flowchart representation of a method 320 a of determiningwhether or not to adjust a realized resiliency factor of an object upondetecting a decrease in a number of available storage entities inaccordance with some implementations. In various implementations, themethod 320 a is implemented as a set of computer readable instructionsthat are executed at a storage system (e.g., the storage system 100shown in FIGS. 1A-1B). For example, in various implementations, themethod 320 a is performed by an ingest entity of the storage system(e.g., the ingest entity 120 shown in FIGS. 1A, 1B and 2). Briefly, themethod 320 a includes detecting a decrease in a number of availablestorage entities, and determining whether or not to adjust a realizedresiliency factor of an object based on the decrease in the number ofavailable storage entities.

As represented by block 332, in various implementations, the method 320a includes storing an object according to a realized resiliency factorthat is equal to a target resiliency factor for the object. Asrepresented by block 334, in various implementations, the method 320 aincludes detecting a decrease in a number of available storage entities.For example, in some implementations, the method 320 a includesdetecting that one or more storage entities has become unavailable. Insome examples, the method 320 a includes detecting the decrease in thenumber of available storage entities when one or more storage entitiesgoes offline (e.g., due to a power outage, or a scheduled maintenance).In some scenarios, the method 320 a includes detecting the decrease inthe number of available storage entities when one or more storageentities fails (e.g., due to a mechanical failure, or a softwarefailure). In some implementations, the method 320 a includes detectingthat the number of available storage entities has decreased from a firstnumber that is above a sum of the target data storage entity count andthe target parity storage entity count to a second number that is belowthe sum.

As represented by block 336, in various implementations, the method 320a includes determining whether the decreased number of available storageentities is sufficient to maintain a realized resiliency factor of anobject that is equal to the target resiliency factor for the object. Forexample, in some implementations, the method 320 a includes determiningwhether the decreased number of available storage entities is greaterthan or equal to a sum of the realized data storage entity count and therealized parity storage entity count. If the decreased number ofavailable storage entities is sufficient to maintain the realizedresiliency factor then the method 320 a proceeds to block 338, otherwisethe method 320 a proceeds to block 340. As represented by block 338, ifthe decreased number of available storage entities are sufficient tomaintain the target resiliency factor, then the method 320 a includesdetermining not to adjust the realized resiliency factor. As representedby block 340, if the decreased number of available storage entities arenot sufficient to maintain the target resiliency factor, then the method320 a includes determining to adjust the realized resiliency factor toan adjusted realized factor that is different from the target resiliencyfactor. In some implementations, the method 320 a includes determiningan adjusted resiliency factor that is as close to the target resiliencyfactor as possible given the decreased number of available storageentities.

FIG. 4B is a flowchart representation of a method 320 b of determiningwhether or not to adjust a realized resiliency factor of an object upondetecting an increase in a number of available storage entities inaccordance with some implementations. In various implementations, themethod 320 b is implemented as a set of computer readable instructionsthat are executed at a storage system (e.g., the storage system 100shown in FIGS. 1A and 1B). For example, in various implementations, themethod 320 b is performed by an ingest entity of the storage system(e.g., the ingest entity 120 shown in FIGS. 1A, 1B and 2). Briefly, themethod 320 b includes detecting an increase in a number of availablestorage entities, and determining whether or not to adjust a realizedresiliency factor of an object based on the increase in the number ofavailable storage entities.

As represented by block 342, in various implementations, the method 320b includes storing an object according to a realized resiliency factorthat is less than a target resiliency factor for the object. Asrepresented by block 344, in various implementations, the method 320 bincludes detecting an increase in a number of available storageentities. For example, in some implementations, the method 320 bincludes detecting that one or more storage entities that was previouslyunavailable has become available. In some examples, the method 320 bincludes detecting the increase in the number of available storageentities when one or more storage entities comes online (e.g., aftersuffering a power outage, or after undergoing a scheduled maintenance).In some scenarios, the method 320 b includes detecting the increase inthe number of available storage entities when one or more storageentities that had previously failed is restored (e.g., via a mechanicalupgrade, or a software patch). In some implementations, the method 320 bincludes detecting an increase in the number of available storageentities from a first number that is below a sum of the target datastorage entity count and the target parity storage entity count to asecond number that is above the sum.

As represented by block 346, in various implementations, the method 320b includes determining whether the increased number of available storageentities is sufficient to adjust the realized resiliency factor to anadjusted resiliency factor that is closer to the target resiliencyfactor. If the increased number of available storage entities issufficient to attain an adjusted resiliency factor that is closer to thetarget resiliency factor then the method 320 b proceeds to block 348,otherwise the method 320 b proceeds to block 350. As represented byblock 348, in some implementations, the method 320 b includesdetermining to adjust the realized resiliency factor to an adjustedresiliency factor that is closer to the target resiliency factor thanthe realized resiliency factor. In some examples, the adjustedresiliency factor is equal to the target resiliency factor. In someexamples, the adjusted resiliency factor is within a threshold range ofthe target resiliency factor. In some implementations, the method 320 bincludes determining an adjusted resiliency factor that is a function ofthe increased number of available storage entities and the targetresiliency factor. As represented by block 350, if the increased numberof storage entities is not sufficient to attain an adjusted resiliencyfactor that is closer to the target resiliency factor, then the method320 b includes determining not to adjust the realized resiliency factor.

FIG. 5 is a block diagram of a server system 500 enabled with variousmodules that are provided to adjust a realized resiliency factor of anobject based on a target resiliency factor for the object in accordancewith some implementations. For example, in various implementations, theserver system 500 is enabled with one or more components of an ingestentity of a storage system (e.g., the ingest entity 120 shown in FIGS.1A, 1B and 2) according to some implementations. While certain specificfeatures are illustrated, those of ordinary skill in the art willappreciate from the present disclosure that various other features havenot been illustrated for the sake of brevity, and so as not to obscuremore pertinent aspects of the implementations disclosed herein. To thatend, as a non-limiting example, in some implementations the serversystem 500 includes one or more processing units (CPUs) 502, a networkinterface 503, a memory 510, a programming interface 508, and one ormore communication buses 504 for interconnecting these and various othercomponents.

In some implementations, the network interface 503 is provided to, amongother uses, establish and maintain a metadata tunnel between a cloudhosted network management system and at least one private networkincluding one or more compliant devices. In some implementations, thecommunication buses 504 include circuitry that interconnects andcontrols communications between system components. The memory 510includes high-speed random access memory, such as DRAM, SRAM, DDR RAM orother random access solid state memory devices; and may includenon-volatile memory, such as one or more magnetic disk storage devices,optical disk storage devices, flash memory devices, or othernon-volatile solid state storage devices. The memory 510 optionallyincludes one or more storage devices remotely located from the CPU(s)502. The memory 510 comprises a non-transitory computer readable storagemedium.

In some implementations, the memory 510 or the non-transitory computerreadable storage medium of the memory 510 stores the following programs,modules and data structures, or a subset thereof including an optionaloperating system 520, a resiliency management module 522, a resiliencydetermination module 524, a resiliency adjustment module 526, and a datastore 528. In various implementations, the resiliency management module522, the resiliency determination module 524, the resiliency adjustmentmodule 526 and the data store 528 are similar to the resiliencymanagement module 122, the resiliency determination module 124, theresiliency adjustment module 126 and the data store 128, respectively,shown in FIG. 2.

The operating system 520 includes procedures for handling various basicsystem services and for performing hardware dependent tasks.

In various implementations, the resiliency determination module 524determines a realized resiliency factor for an object (e.g., therealized resiliency factor 130 shown in FIGS. 1A and 2). To that end, invarious implementations, the resiliency determination module 524includes instructions and/or logic 524 a, and heuristics and metadata524 b. In various implementations, the resiliency management module 522determines whether or not to adjust the realized resiliency factor of anobject to an adjusted resiliency factor (e.g., the adjusted resiliencyfactor 230 shown in FIGS. 1B and 2) based on a target resiliency factor(e.g., the target resiliency factor 30 shown in FIGS. 1A and 2). Invarious implementations, the resiliency management module 522 manages aresiliency factor of an object. To that end, in various implementations,the resiliency management module 522 includes instructions and/or logic522 a, and heuristics and metadata 522 b. In various implementations,the resiliency adjustment module 526 adjusts the realized resiliencymodule to the adjusted resiliency factor. In some implementations, theadjusted resiliency factor is equal to the target resiliency factor. Insome implementations, the adjusted resiliency factor is closer to thetarget resiliency factor than the realized resiliency factor. In variousimplementations, the adjusted resiliency factor is a function of thetarget resiliency factor and a number of available storage entities inthe storage system. To that end, in various implementations, theresiliency adjustment module 526 includes instructions and/or logic 526a, and heuristics and metadata 526 b. In various implementations, theserver system 500 performs the method 300 shown in FIG. 3, the method320 a shown in FIG. 4A, and/or the method 320 b shown in FIG. 4B.

While various aspects of implementations within the scope of theappended claims are described above, it should be apparent that thevarious features of implementations described above may be embodied in awide variety of forms and that any specific structure and/or functiondescribed above is merely illustrative. Based on the present disclosureone skilled in the art should appreciate that an aspect described hereinmay be implemented independently of any other aspects and that two ormore of these aspects may be combined in various ways. For example, anapparatus may be implemented and/or a method may be practiced using anynumber of the aspects set forth herein. In addition, such an apparatusmay be implemented and/or such a method may be practiced using otherstructure and/or functionality in addition to or other than one or moreof the aspects set forth herein.

It will also be understood that, although the terms “first,” “second,”etc. may be used herein to describe various elements, these elementsshould not be limited by these terms. These terms are only used todistinguish one element from another. For example, a first contact couldbe termed a second contact, and, similarly, a second contact could betermed a first contact, which changing the meaning of the description,so long as all occurrences of the “first contact” are renamedconsistently and all occurrences of the second contact are renamedconsistently. The first contact and the second contact are bothcontacts, but they are not the same contact.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the claims. Asused in the description of the embodiments and the appended claims, thesingular forms “a”, “an” and “the” are intended to include the pluralforms as well, unless the context clearly indicates otherwise. It willalso be understood that the term “and/or” as used herein refers to andencompasses any and all possible combinations of one or more of theassociated listed items. It will be further understood that the terms“comprises” and/or “comprising,” when used in this specification,specify the presence of stated features, integers, steps, operations,elements, and/or components, but do not preclude the presence oraddition of one or more other features, integers, steps, operations,elements, components, and/or groups thereof.

As used herein, the term “if” may be construed to mean “when” or “upon”or “in response to determining” or “in accordance with a determination”or “in response to detecting,” that a stated condition precedent istrue, depending on the context. Similarly, the phrase “if it isdetermined [that a stated condition precedent is true]” or “if [a statedcondition precedent is true]” or “when [a stated condition precedent istrue]” may be construed to mean “upon determining” or “in response todetermining” or “in accordance with a determination” or “upon detecting”or “in response to detecting” that the stated condition precedent istrue, depending on the context.

What is claimed is:
 1. A method comprising: at an enterprise objectstorage system including a plurality of storage entities, eachconfigured to store data on a block basis, and one or more processors:obtaining a target resiliency factor and a realized resiliency factorfor an object, wherein the target resiliency factor indicates a targetnumber of data storage entities and a target number of parity storageentities for the object, and the realized resiliency factor indicates arealized number of data storage entities and a realized number of paritystorage entities allocated to the object; determining whether or not toadjust the realized resiliency factor for the object based on the targetresiliency factor and a number of available storage entities in theenterprise object storage system; adjusting the realized resiliencyfactor to an adjusted resiliency factor in response to determining toadjust the realized resiliency factor, wherein the adjusted resiliencyfactor indicates an adjusted number of data storage entities and anadjusted number of parity storage entities for the object; andre-distributing, according to the adjusted realized resiliency, objectdata associated with the object across the adjusted number of datastorage entities and parity information associated with the objectacross the adjusted number of parity storage entities.
 2. The method ofclaim 1, wherein: the object includes a file that refers to a data assetpresentable to a user; the object data associated with the objectre-distributed across the adjusted number of data storage entities isstored across data blocks; and the parity information associated withthe object re-distributed across the adjusted number of parity storageentities is stored across parity blocks.
 3. The method of claim 1,wherein re-distributing, according to the adjusted realized resiliency,the object data associated with the object across the adjusted number ofdata storage entities and the parity information associated with theobject across the adjusted number of parity storage entities includes:storing the object data across the adjusted number of data storageentities as re-distributed object data; synthesizing the parityinformation associated with the object based on the re-distributedobject data and the adjusted resiliency factor; and storing the parityinformation across the adjusted number of parity storage entities. 4.The method of claim 1, wherein re-distributing, according to theadjusted realized resiliency, the object data associated with the objectacross the adjusted number of data storage entities and the parityinformation associated with the object across the adjusted number ofparity storage entities includes: determining that the adjusted numberof data storage entity is greater than the realized number of datastorage entities; moving a portion of the object data associated withthe object to storage entities that were not previously storing theobject data; and spreading the object data across the adjusted number ofdata storage entities.
 5. The method of claim 1, whereinre-distributing, according to the adjusted realized resiliency, theobject data associated with the object across the adjusted number ofdata storage entities and the parity information associated with theobject across the adjusted number of parity storage entities includes:determining that the adjusted number of data storage entity is less thanthe realized number of data storage entities; removing a portion of theobject data from storage entities that are in excess of the adjustednumber of data storage entities; and limiting the object data to theadjusted number of data storage entities.
 6. The method of claim 1,further comprising: counting the realized number of data storageentities that store data segments of the object data; counting therealized number of parity storage entities that store parity segments ofthe object data; calculating the realized resiliency factor based on therealized number of data storage entities and the realized number ofparity storage entities; and storing the realized resiliency factor in anon-transitory memory.
 7. The method of claim 6, wherein obtaining therealized resiliency factor for the object comprises: obtaining therealized resiliency factor for the object from the non-transitorymemory.
 8. An enterprise object storage system comprising: a pluralityof storage entities that are configured to store objects in adistributed manner; and an ingest entity including one or moreprocessors configured to execute computer readable instructions includedon a non-transitory memory and a non-transitory memory includingcomputer readable instructions that, when executed by the one or moreprocessors, cause the ingest entity to: obtain a target resiliencyfactor and a realized resiliency factor for an object, wherein thetarget resiliency factor indicates a target number of data storageentities and a target number of parity storage entities for the object,and the realized resiliency factor indicates a realized number of datastorage entities and a realized number of parity storage entitiesallocated to the object; determine whether or not to adjust the realizedresiliency factor for the object based on the target resiliency factorand a number of available storage entities in the enterprise objectstorage system; adjust the realized resiliency factor to an adjustedresiliency factor in response to determining to adjust the realizedresiliency factor, wherein the adjusted resiliency factor indicates anadjusted number of data storage entities and an adjusted number ofparity storage entities for the object; and re-distribute, according tothe adjusted realized resiliency, object data associated with the objectacross the adjusted number of data storage entities and parityinformation associated with the object across the adjusted number ofparity storage entities.
 9. The enterprise object storage system ofclaim 8, wherein: the object includes a file that refers to a data assetpresentable to a user; the object data associated with the objectre-distributed across the adjusted number of data storage entities isstored across data blocks; and the parity information associated withthe object re-distributed across the adjusted number of parity storageentities is stored across parity blocks.
 10. The enterprise objectstorage system of claim 8, wherein re-distributing, according to theadjusted realized resiliency, the object data associated with the objectacross the adjusted number of data storage entities and the parityinformation associated with the object across the adjusted number ofparity storage entities includes: storing the object data across theadjusted number of data storage entities as re-distributed object data;synthesizing the parity information associated with the object based onthe re-distributed object data and the adjusted resiliency factor; andstoring the parity information across the adjusted number of paritystorage entities.
 11. The enterprise object storage system of claim 8,wherein re-distributing, according to the adjusted realized resiliency,the object data associated with the object across the adjusted number ofdata storage entities and the parity information associated with theobject across the adjusted number of parity storage entities includes:determining that the adjusted number of data storage entity is greaterthan the realized number of data storage entities; moving a portion ofthe object data associated with the object to storage entities that werenot previously storing the object data; and spreading the object dataacross the adjusted number of data storage entities.
 12. The enterpriseobject storage system of claim 8, wherein re-distributing, according tothe adjusted realized resiliency, the object data associated with theobject across the adjusted number of data storage entities and theparity information associated with the object across the adjusted numberof parity storage entities includes: determining that the adjustednumber of data storage entity is less than the realized number of datastorage entities; removing a portion of the object data from storageentities that are in excess of the adjusted number of data storageentities; and limiting the object data to the adjusted number of datastorage entities.
 13. The enterprise object storage system of claim 8,wherein the ingest entity is further configured to: count the realizednumber of data storage entities that store data segments of the objectdata; count the realized number of parity storage entities that storeparity segments of the object data; calculate the realized resiliencyfactor based on the realized number of data storage entities and therealized number of parity storage entities; and store the realizedresiliency factor in the non-transitory memory.
 14. The enterpriseobject storage system of claim 13, wherein obtaining the realizedresiliency factor for the object comprises: obtaining the realizedresiliency factor for the object from the non-transitory memory.
 15. Adevice comprising: a processor configured to execute computer readableinstructions included on a non-transitory memory; and the non-transitorymemory including computer readable instructions, that when executed bythe processor, cause the device to: obtain a target resiliency factorand a realized resiliency factor for an object, wherein the targetresiliency factor indicates a target number of data storage entities anda target number of parity storage entities for the object, and therealized resiliency factor indicates a realized number of data storageentities and a realized number of parity storage entities allocated tothe object; determine whether or not to adjust the realized resiliencyfactor for the object based on the target resiliency factor and a numberof available storage entities in the enterprise object storage system;adjust the realized resiliency factor to an adjusted resiliency factorin response to determining to adjust the realized resiliency factor,wherein the adjusted resiliency factor indicates an adjusted number ofdata storage entities and an adjusted number of parity storage entitiesfor the object; and re-distribute, according to the adjusted realizedresiliency, object data associated with the object across the adjustednumber of data storage entities and parity information associated withthe object across the adjusted number of parity storage entities. 16.The device of claim 15, wherein: the object includes a file that refersto a data asset presentable to a user; the object data associated withthe object re-distributed across the adjusted number of data storageentities is stored across data blocks; and the parity informationassociated with the object re-distributed across the adjusted number ofparity storage entities is stored across parity blocks.
 17. The deviceof claim 15, wherein re-distributing, according to the adjusted realizedresiliency, the object data associated with the object across theadjusted number of data storage entities and the parity informationassociated with the object across the adjusted number of parity storageentities includes: storing the object data across the adjusted number ofdata storage entities as re-distributed object data; synthesizing theparity information associated with the object based on there-distributed object data and the adjusted resiliency factor; andstoring the parity information across the adjusted number of paritystorage entities.
 18. The device of claim 15, wherein re-distributing,according to the adjusted realized resiliency, the object dataassociated with the object across the adjusted number of data storageentities and the parity information associated with the object acrossthe adjusted number of parity storage entities includes: determiningthat the adjusted number of data storage entity is greater than therealized number of data storage entities; moving a portion of the objectdata associated with the object to storage entities that were notpreviously storing the object data; and spreading the object data acrossthe adjusted number of data storage entities.
 19. The device of claim15, wherein re-distributing, according to the adjusted realizedresiliency, the object data associated with the object across theadjusted number of data storage entities and the parity informationassociated with the object across the adjusted number of parity storageentities includes: determining that the adjusted number of data storageentity is less than the realized number of data storage entities;removing a portion of the object data from storage entities that are inexcess of the adjusted number of data storage entities; and limiting theobject data to the adjusted number of data storage entities.
 20. Thedevice of claim 15, wherein the non-transitory memory further includesthe computer readable instructions, that when executed by the processor,cause the device to: count the realized number of data storage entitiesthat store data segments of the object data; count the realized numberof parity storage entities that store parity segments of the objectdata; calculate the realized resiliency factor based on the realizednumber of data storage entities and the realized number of paritystorage entities; and store the realized resiliency factor in thenon-transitory memory.